Credit Risk Management

Credit Risk Management when looked at from the perspective of corporate credit management is simply defined as the management of the likelihood that a borrower (of the bank) or counterparty will default on its credit or fail to meet its obligations in accordance with the agreed terms.

This is one of the fundamental contributing factors to our present economic crisis. Lenders develop estimates or projections of future returns just like any organized business. For a bank to make money from their lending divisions they need to take into account:

The Cost of Lending Money

The Return they receive from Interest, Penalties, and Fees

The Amount of Credit that will not be Repaid

Credit Risk Management was not taking place to the extent it should have been with the majority of our countries major banks during the majority of the first decade this millennium. This is evident when we look at the amount of credit card debt and necessary credit card debt relief that has been estimated at 5 times the amounts of last decade.

For example, if you were to ignore the risk that your credit would not be repaid you would come to the simple conclusion that the more lending you do the more profitable it will be. This is because most lenders receive their money from the government at discounted rates and issue that same money at a higher rate in the form of loans. This simple idea may lead some to believe, whether out of greed or lack of thought that credit risk management only provides information that limits the revenues of the lender. This is an expense to lenders and it should be minimized.

Similar to insurance policies, hedging and loss mitigation: if the benefits of limiting losses are ignored it is undeniable that they have little to know revenue generation and they do generate expenses. Unfortunately, it is not difficult to look back at the first decade of this millennium and picture all the loss that could have been prevented if Credit Risk Management departments spent more time or more accurately followed traditional algorithms for predicting creditworthiness and likelihood of default.